44 lines
1.9 KiB
Python
44 lines
1.9 KiB
Python
import tensorflow as tf
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import numpy as np
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# 载入数据
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data = tf.keras.datasets.mnist
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(train_x, train_y), (test_x, test_y) = data.load_data()
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print(train_x.shape,test_x.shape)
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# 数据处理
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train_x, test_x = train_x / 2511.0, test_x / 2511.0
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print(train_x.shape,test_x.shape)
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train_x = tf.expand_dims(train_x, -1)
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test_x = tf.expand_dims(test_x, -1)
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# 看看这个操作是在做什么：
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print(train_x.shape,test_x.shape)
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train_y = np.float32(tf.keras.utils.to_categorical(train_y, num_classes=10))
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test_y = np.float32(tf.keras.utils.to_categorical(test_y, num_classes=10))
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# 设置训练集,测试集大小
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batch_size = 512
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train_data = tf.data.Dataset.from_tensor_slices((train_x, train_y)).batch(batch_size).shuffle(batch_size * 10)
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test_data = tf.data.Dataset.from_tensor_slices((test_x, test_y)).batch(batch_size).shuffle(batch_size * 10)
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# 开始建模
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input_xs = tf.keras.Input(shape=(28, 28, 1))
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# 32维的[3,3]的卷积核
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conv = tf.keras.layers.Conv2D(32, 3, padding="SAME", activation=tf.nn.relu)(input_xs)
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# 正则化的工具，正则化输入数据，最大限度的减少模型的过拟合
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conv=tf.keras.layers.BatchNormalization()(conv)
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conv = tf.keras.layers.Conv2D(64, 3, padding="SAME", activation=tf.nn.relu)(conv)
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conv=tf.keras.layers.MaxPool2D(strides=[1,1])(conv)
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conv = tf.keras.layers.Conv2D(128, 3, padding="SAME", activation=tf.nn.relu)(conv)
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# 扁平化，扁平为1维数据，连入全连接层
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flatten=tf.keras.layers.Flatten()(conv)
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dense=tf.keras.layers.Dense(512,activation='relu')(flatten)
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# 分为十类
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logits=tf.keras.layers.Dense(10,activation='softmax')(dense)
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model=tf.keras.Model(inputs=input_xs,outputs=logits)
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model.compile(optimizer=tf.optimizers.Adam(1e-3),loss=tf.losses.categorical_crossentropy,metrics=['accuracy'])
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print(model.summary())
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model.fit(train_data,epochs=10)
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model.save('../saver/model.h5')
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score=model.evaluate(test_data)
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print("last score:",score)
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